Google is going to be beaten to the punch. For years consumers have eagerly awaited the public launch of the firm’s: autonomous pods that will leave us free to read, watch TV or work on tasks other than driving. Now it seems as if traditional car manufacturers are about to take the lead.
Last week, car companies from around the world lined up at the Consumer Electronics Show (CES) in Las Vegas to announce their latest technology and investment in autonomous driving.
General Motors said that it would spend $500 million with car-hailing service Lyft to build “an integrated network of on-demand autonomous vehicles in the US”. Toyota is building a new research institute to work on autonomy, while Audi, BMW, Ford and others also announced progress in their efforts to develop driverless cars. Mercedes has developed a self-driving research vehicle (pictured above) and taxi app Uber has already announced billions of dollars in autonomous car investment.
To operate safely, driverless cars. They do this using a range of sensors, but to pick out hazards effectively, the car must be able to separate them from the background. This means the car needs a detailed, static picture of the world with which to compare what it sees – in other words, a map.
You might think this would give Google an edge – it already has a vast set of maps and the infrastructure for updating them. Its prototype autonomous cars have driven farther than any other company’s offering, even on some public roads in California and Texas. But Google has a way to go: the digital maps the company does so well must be augmented with more data if they are to guide autonomous cars.
On the map
At the moment, Google doesn’t have much of this extra data and neither does anyone else. But now, car manufacturers are banding together to make their own maps, and it looks as if they may be in a better position to do so than the search giant.
Raj Rajkumar of Carnegie Mellon University in Pittsburgh, Pennsylvania, identifies multiple ways in which companies can build maps for their robot cars. The first is Google’s “do everything” approach: the company controls its entire driverless car operations, gathering the map data itself and processing it for the intelligent software that drives its cars. Google could use Street View to get its map data, collected by a dedicated fleet of cars.
But it’s expensive and impractical to run Street View cars for the sole purpose of repeatedly scanning roads to keep maps up-to-date. For example, Street View has mapped a busy street in London, the A201 next to St Paul’s Cathedral, seven times since it began in 2008. But the street I live on, in a Greenwich suburb, has only ever been mapped once, last year.
Another is the approach taken by Here Maps, a former division of Nokia. Like Google, Here drives its own mapping cars around cities and motorways. But Here aims to license its data to car manufacturers that are planning to build autonomy into their vehicles.
Here was recently bought by a consortium of big car-makers – and if that wasn’t worrying enough for Google, the companies have another trick up their sleeves. They plan to use the sensors on new car models to collectively gather mapping data, rather than having an expensive fleet of dedicated vehicles.
Expecting the unexpected
Announcing their acquisition of Here Maps last year, Daimler, Volkswagen and BMW stated that “the high-precision cameras and sensors installed in modern cars are the digital eyes for updating mobility data and maps”.
“For the automotive industry this is the basis for new assistance systems and ultimately fully autonomous driving,” the companies said.
This data will help autonomous cars; instead of being stumped by roadworks, cars using Here will have had their maps updated in real time and will know to simply go around.
And the companies working with Here aren’t the only ones with this idea: General Motors is plugging sensors into its new cars to crowdsource data itself. The company has joined forces with an Israeli start-up called Mobileye, which makes software for driverless cars using cameras and image processing, rather than Google’s more expensive lidar system. Tesla also uses Mobileye’s technology in its autonomous driving software, which was recently released to all of its Model S cars through a software update.
General Motors is well placed for crowdsourcing because it has the scale to collect large amounts of data quickly. The company sells several million cars every year, many of which have cameras and networking equipment that will let them contribute to maps. The firm’s prospects for fully autonomous driving improve every minute that its customers drive around.
“Crowdsourcing is the traditional car companies’ very, very big advantage,” says Rajkumar. “There’s an interesting competition ahead.”
It’s an unusual position for Google, whose power has always come from having more data than its competition. “When it comes to information about the physical world that we live in, Google doesn’t really have a presence,” says Rajkumar.
Indeed it is Toyota, not Google, that has the lion’s share of the patents for self-driving cars,. The Japanese company recently pumped $1 billion into a new research institute dedicated to using
In a speech at CES, Gill Pratt, CEO of the new institute, indirectly challenged Google’s achievements so far. “Most of what has been collectively accomplished has been relatively easy because most driving is easy,” he said. “Where we need autonomy to help us is when the driving is difficult. And it’s this hard part that we intend to address.”
Image credit: Daimler AG – Global Communications Mercedes-Benz Cars
The heart of a self-driving car
Autonomous cars will need a host of specialised hardware and artificial intelligence to work safely. Last week, Nvidia, a Californian company best known for making computer graphics hardware, announced a computer designed specifically for self-driving cars.
The box will sit at the core of the cars, processing data from up to 12 cameras, as well as light-based radar and other sensors. It combines views to detect and identify objects, determine where the car is relative to its surroundings and then calculate the optimal path for safe travel.
Much of the learning software needed to give cars autonomy is openly available: anyone can download and useor Caffe, two of the most widely used machine learning systems.
With the hardware and software available to create intelligent driving systems, the most valuable missing piece is data. And for once, Google is not the only firm with access to that.
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